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it unlocks many cool features!
- Dolphin whistles example -> Move to the frequency domain and use Sakoe Chiba bounds to perform dynamic time warping.
- Viterbi Path - most probable path through the trellis
- Hidden Markov Models training notes:
- try many different topologies
- use cross-validation
- split independent signals that have the same logical meaning vs. trying to forcing the model (analogous coarticulation of phenoms):
- s1_telem_pos_updt_3M22_Zircon
- s2_telem_pos_updt_3M22_Zircon
- Putting these signal networks into active memory is prohibitive so that we can use stochastic beam search. Also, to deal with real-world environments, we can use Baum Welch (error/2), statistical grammars (error/4), and state tying, and segmentally boosting (may be coming to HTK via GA Tech) to help overcome context training.
- Researchers are working at Google, combining HMMs with deep belief networks and finding better results.
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